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Simon Benjamin

Professor Simon Benjamin
Professor of Quantum Technologies

Department of Materials
University of Oxford
16 Parks Road
Oxford OX1 3PH
UK

Tel: +44 1865 273732 (Room 195.40.02)
Tel: +44 1865 273777 (reception)
Fax: +44 1865 273789 (general fax)

QuNaT Group

Summary of Interests

1. New technologies that explaoit quantum physics: quantum sensors, quantum communications, and quantum computing. Theory to support the developement of these technologies on various platforms, including novel silicon and diamond based materials. 

2. Energy harvesting, transfer and storage understood at the quantum level. Modelling of energy flow phenomena in both artificial and living (e.g. photosynthetic) systems. 

Current Research Projects

Coherent Control of Spin Systems
Dr. S.C. Benjamin, Dr. B.W. Lovett*, Dr. E.M. Gauger
We are studying the quantum properties of nuclear and electron spins, primarily in molecular systems. Our aim is to provide theory that will allow for the control small numbers of spins, such that the quantum coherence is preserved for as long as possible. We collaborate with the Quantum Spin Dynamics experimental group in London (http://www.ucl.ac.uk/qsd), and together we demonstrated that the quantum state of an electron spin can be transferred coherently to a nuclear spin, thus increasing the coherence time. We are now working on optical methods for further improving coherence, and for coupling several spins together. (*Heriot-Watt University)

Architectures and materials for robust and scalable quantum technologies
Dr. S.C. Benjamin, Ms Naomi Nickerson
Today's computers may seem very powerful, but their designs do not take advantange of the enormous potential power of quantum physics. We know that it is possible, in principle, to build an entirely new class of technology that would harness effects like quantum superposition and quantum entanglement in order to profoundly outperform all conventional machines (at least for certain key tasks). However such technologies are very challenging to build in reality. It particular it is difficult to take the small prototype systems in the laboratory and scale them up to the point that they start to exceed the capacities of conventional technologies.  This project is about finding ways to build these technologies that are robust, in the sense that they can operate with realisitic levels of imperfection, and also scalable -- so that once you have a few components working together, it is straightforward to add more and more. For example: One approach would be to build the large machine by networking together many simple processor cells, thus avoiding the need to create a single complex structure. See for example our open Nature Communications paper: http://www.nature.com/ncomms/journal/v4/n4/full/ncomms2773.html

Quantum energy calculations for artificial and biological nanostructures
Dr. S.C. Benjamin, Dr. B.W. Lovett*, Dr. E.M. Gauger, Mr Higgins, Mr Pollock
In order to best understand how to engineer molecular scale systems that can harvest, transfer and store energy, it is necessary to understand energy transfer at the quantum level. There is evidence to suggest that Nature's molecular technologies, for example the structures involved in photosynthesis, perform energy transfer in a way that involves quantum coherence. This is a surprise since quantum effects are usually thought to be difficult to achieve and more the province of the physics laboratory than a "warm and wet" biological system. We are developing new analytic and numerical techniques to understand energy transfer as a fully quantum mechanical process, and aiming to apply this both to natural systems and to artificial structures created by our experimental collaborators. The task is challenging, but the answers may eventually allow us to design highly efficient molecular scale technologies.(*Heriot-Watt University)

Quantum superposition in large systems
Dr. S.C. Benjamin, E. Gauger, Professor G.A.D. Briggs, G. Knee
This is a theoretical project looking at the possibilities inherent in creating quantum superpositions of large objects such as massive molecules or SQUIDs and similar. A key theoretical tool is be the Leggett-Garg inequality, which tests to see if a system needs quantum physics to describe its behavoir. We are now buildings on the early success of this project, which we reported in this open Nature Communications paper: http://www.nature.com/ncomms/journal/v3/n1/full/ncomms1614.html

4 public active projects

Research Publications

Nickerson, N., Li, Y. and Benjamin, S. C., 'Topological quantum computing with a very noisy network and local error rates approaching one percent' Nature Communications 4, Article 1756 (2013) OPEN article http://www.nature.com/ncomms/journal/v4/n4/full/ncomms2773.html

Li, Y., Barrett, S., Stace, T. and Benjamin, S. 'Long range failure-tolerant entanglement distribution' New J. Phys. 15 023012 (2013)

Knee, G. C., Briggs, G. A. D., Benjamin, S. C. and Gauger, E. M., 'Quantum sensors based on weak-value amplification cannot overcome decoherence, Phys. Rev. A 87, 012115 (2013)

Ping, Y., Lovett, B. W., Benjamin, S. C. and Gauger, E. M., Practicality of spin chain 'wiring' in diamond quantum technologies, Phys. Rev. Lett. 110, 100503 (2013)

Ping, Y., Gauger, E. M., and Benjamin, S. C. 'Measurement-based quantum computing with a spin ensemble coupled to a stripline cavity' New J. Phys. 14, 013030 (2012)

Knee, G. et al, 'Violation of a Leggett–Garg inequality with ideal non-invasive measurements' Nature Communications 3, Article number: 606 (2012) OPEN article http://www.nature.com/ncomms/journal/v3/n1/full/ncomms1614.html

Projects Available

*Architectures and algorithms for near-future quantum machine learning and optimization
Prof S C Benjamin

This is a theory project to be hosted in Prof. Simon Benjamin’s Quantum and Nanotechnology Theory Group (see qunat.org).

Background: At present in the UK and worldwide there is a major drive towards developing quantum technologies. These are devices that harness the deeper principles of quantum physics in order to outperform conventional technologies. The most challenging and perhaps the most important goal of this field is to create quantum computers — machines that store and process qubits (quantum bits) rather than bits.

Oxford is leading one of four UK “Quantum Hubs”. Each Hub is an alliance of universities that are working to accelerate progress towards a particular kind of quantum technology; the Oxford-led Hub is called “Networked Quantum Information Technologies” and is focused mainly on creating quantum computers (see NQIT.org).

Studentship details: This studentship is concerned with finding new applications for quantum computers. The two main areas of investigation will be machine learning and optimisation. Machine learning is a very active field of research for conventional computer science, with applications in areas ranging from big data analysis through to medicine and self-driving cars. The aim of this project will be to seek for opportunities to use quantum systems to accelerate important tasks in machine learning, including for example the training of neural networks. Meanwhile ‘optimisation’ refers to finding the best solution to a complex problem with many variables — a practical example might be routing supply vehicles for a large courier company. Solving optimisation problems is very important commercially, and it has been suggested that early quantum technologies (including the machines that are already sold by the company D-Wave) may be able to perform optimisation more efficiently than conventional computers. This will be explored with analytic theory, numerical simulations, and (very probably) by directly using D-Wave hardware which the QuNaT group has access to.

This project would suit a student with a strong physics, mathematics or computer science background. Prior experience in machine learning or optimisation is not required but an enthusiasm to learn is essential! Oxford has a very large machine learning community and offers many courses etc.

Candidates are considered in the January 2017 admissions cycle which has an application deadline of 20 January 2017.

The NQIT Hub is funded by an award to a consortium of nine universities and is supported by a number of commercial and governmental partners, including the EPSRC. This 3-year studentship will provide full fees and maintenance for a student as home fee status (this includes an EU student who has spent the previous three years (or more) in the UK undertaking undergraduate study). The stipend will be £14,296 per year. Other EU students should read the guidance at http://www.materials.ox.ac.uk/admissions/postgraduate/eu.html for further information about eligibility.

Any questions concerning the project can be addressed to Professor Simon Benjamin (simon.benjamin@materials.ox.ac.uk). General enquiries on how to apply can be made by e mail to graduate.studies@materials.ox.ac.uk. You must complete the standard Oxford University Application for Graduate Studies. Further information and an electronic copy of the application form can be found at http://www.ox.ac.uk/admissions/postgraduate_courses/apply/index.html.

Also see homepages: Simon Benjamin

Imperfect quantum technology: Finding applications for first generation quantum computers.
Prof S C Benjamin

Simon Benjamin has an ongoing theory project which uses conventional supercomputers to predict the behaviour of 1st generation quantum computers including their limitations and flaws. The aim is to find applications for these powerful but imperfect systems. While there is no specific earmarked studentship for this topic, Simon welcomes applications and he will explore funding options with successful applicant(s).
Regarding funding, note that applicants will be considered automatically for certain Oxford scholarships for which they are eligible. There is also the option to use our online 'funding search tool’ to identify any Oxford scholarships for which they are eligible and which require a separate application.

Background: Many research groups around the world are getting close to realizing the first generation of a profoundly powerful new class of technology: quantum computers. Building such a machine means learning to control qubits (quantum bits). Different approaches are being tried: qubits may be individual atoms, or nanostructures in diamond, or superconducting loops. But all have one thing in common: the control we can achieve is far lower than the control we have over bits in conventional computers. The first generation of quantum computers will therefore be imperfect, by comparison to our reliable conventional technologies, but they will still have the potential to be vastly more powerful. 

The project: Since 1st generation quantum computers will have imperfect qubits, therefore one must look for tasks that can be successfully performed even in the presence of small errors. A priority would be to study certain physical systems that Oxford experimentalists are working on, especially a hybrid matter-light networks, but the approach would also apply to pure optical processors, monolithic matter systems, and some alternative approaches such as the D-Wave systems. 

There is some interesting work from Oxford (e.g. Scientific Reports volume 6, article 32940 and arXiv:1611.09301) and various other groups worldwide (for one example, arXiv:1602.01857) which suggest that that indeed small errors are not a “show stopper” and thus we should be able to put first generation quantum computers to work on useful tasks. But much more work needs to be done here. 

We use conventional supercomputers, including the Oxford-based dedicated NQIT cluster operated by ARC which has a value of ~£600,000 to discover which of the many tasks that are suggested for quantum computers can in fact operate successfully in the presence of errors. The work will be tied closely to experimental teams in the UK and internationally so that there are opportunities to influence the design of emerging machines — if, for example, we discover that a particular task can work well providing that measurement errors are below a certain threshold, then this can inform the priorities for the experimental teams. 

This project would suit a student with a strong physics background who wants to work on a theory topic – someone who is interested in analytic “pen and paper” theoretical analysis as well as programming for numerical simulations on high powered computers. 

Also see homepages: Simon Benjamin

*Efficient quantum device tuning using machine learning
Edward Laird, Natalia Ares, Andrew Briggs, Simon Benjamin

Fault-tolerant quantum computers will require hundreds to millions of physical qubits to be operated with high fidelity. Inevitable hardware imperfections must be tuned away through iterative interplay of characterization, simulation, and parameter refinement, with each data point informing the decision of what to measure next. The technology is only scalable if this task can be efficiently automated. In the language of computer science, this is a Bayesian optimization problem. Recent progress in machine learning, currently one of the most rapidly developing fields of computing, makes it possible to automate the entire process. This project will apply these new techniques experimentally, working with leaders in machine learning. This is primarily an experimental project, but with substantial theoretical and computational elements. We seek candidates with a strong physics background, but with good knowledge of programming.

The focus will be electron spin qubits in gate-defined GaAs quantum dots. These are an ideal testbed because the physics is known and the dot potential and tunnel barriers are conveniently optimized by tuning gate voltages. Nonetheless, tuning a simple device by hand takes days to weeks, which is clearly not scalable.

The goal of this project is to develop a machine to automatically tune a singlet-triplet qubit in a double quantum dot. The machine will use electrical measurements of the quantum dot to deduce device parameters in the most efficient way, and then adjust gate voltages to optimise them. Inevitably, device imperfections lead to trade-offs in how it is tuned, and we will use simulations of small qubit clusters to identify how to optimise these to make spin qubits useful even in the presence of errors. This project makes use of new equipment in Oxford, including a facility for hardware-in-the-loop testing of quantum technology, and the NQIT computing facility. The applications of this approach will, we hope, ultimately extend to many areas of experimental science.

Candidates are considered in the January 2017 admissions cycle which has an application deadline of 20 January 2017.

The NQIT Hub is funded by an award to a consortium of nine universities and is supported by a number of commercial and governmental partners, including the EPSRC. This 3-year studentship will provide full fees and maintenance for a student as home fee status (this includes an EU student who has spent the previous three years (or more) in the UK undertaking undergraduate study). The stipend will be £14,296 per year. Other EU students should read the guidance at http://www.materials.ox.ac.uk/admissions/postgraduate/eu.html for further information about eligibility.

Any questions concerning the project can be addressed to Dr Edward Laird (edward.laird@materials.ox.ac.uk). General enquiries on how to apply can be made by e mail to graduate.studies@materials.ox.ac.uk. You must complete the standard Oxford University Application for Graduate Studies. Further information and an electronic copy of the application form can be found at http://www.ox.ac.uk/admissions/postgraduate_courses/apply/index.html.

Also see homepages: Natalia Ares Simon Benjamin Andrew Briggs Edward Laird

Also see a full listing of New projects available within the Department of Materials.